code: def test_hifigan_generator_and_discriminator_and_loss(
      dict_g, dict_d, dict_loss, average, include
  ):
      batch_size = 2
      batch_length = 128
      args_g = make_hifigan_generator_args(**dict_g)
      args_d = make_hifigan_multi_scale_multi_period_discriminator_args(
          **dict_d
      )
      args_loss = make_mel_loss_args(**dict_loss)
      y = torch.randn(batch_size, 1, batch_length)
      c = torch.randn(
          batch_size,
          args_g["in_channels"],
          batch_length // np.prod(args_g["upsample_scales"]),
      )
      g = None
      if args_g.get("global_channels") is not None:
          g = torch.randn(batch_size, args_g["global_channels"], 1)
      model_g = HiFiGANGenerator(**args_g)
      model_d = HiFiGANMultiScaleMultiPeriodDiscriminator(**args_d)
      aux_criterion = MelSpectrogramLoss(**args_loss)
      feat_match_criterion = FeatureMatchLoss(
          average_by_layers=average,
          average_by_discriminators=average,
          include_final_outputs=include,
      )
      gen_adv_criterion = GeneratorAdversarialLoss(
          average_by_discriminators=average,
      )
      dis_adv_criterion = DiscriminatorAdversarialLoss(
          average_by_discriminators=average,
      )
      optimizer_g = torch.optim.AdamW(model_g.parameters())
      optimizer_d = torch.optim.AdamW(model_d.parameters())
  
      # check generator trainable
      y_hat = model_g(c, g=g)
      p_hat = model_d(y_hat)
      aux_loss = aux_criterion(y_hat, y)
      adv_loss = gen_adv_criterion(p_hat)
      with torch.no_grad():
          p = model_d(y)
      fm_loss = feat_match_criterion(p_hat, p)
      loss_g = adv_loss + aux_loss + fm_loss
      optimizer_g.zero_grad()
      loss_g.backward()
      optimizer_g.step()
  
      # check discriminator trainable
      p = model_d(y)
      p_hat = model_d(y_hat.detach())
      real_loss, fake_loss = dis_adv_criterion(p_hat, p)
      loss_d = real_loss + fake_loss
      optimizer_d.zero_grad()
      loss_d.backward()
      optimizer_d.step()
  
file_path: espnet/test/espnet2/gan_tts/hifigan/test_hifigan.py
imports: import numpy
  import pytest
  import torch
  from espnet2.gan_tts.hifigan import HiFiGANGenerator, HiFiGANMultiScaleMultiPeriodDiscriminator
  from espnet2.gan_tts.hifigan.loss import DiscriminatorAdversarialLoss, FeatureMatchLoss, GeneratorAdversarialLoss, MelSpectrogramLoss
label_desc: Use image node_id 4 for calling a global function with example usage: test_hifigan_generator_and_discriminator_and_loss(dict_g, dict_d, dict_loss, average, include) without return types